{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T20:34:52Z","timestamp":1782333292544,"version":"3.54.5"},"reference-count":30,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,5,10]],"date-time":"2021-05-10T00:00:00Z","timestamp":1620604800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,5,10]],"date-time":"2021-05-10T00:00:00Z","timestamp":1620604800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,5,10]],"date-time":"2021-05-10T00:00:00Z","timestamp":1620604800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100003696","name":"Electronics and Telecommunications Research Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003696","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001321","name":"National Research Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001321","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,5,10]]},"DOI":"10.1109\/infocom42981.2021.9488816","type":"proceedings-article","created":{"date-parts":[[2021,7,27]],"date-time":"2021-07-27T00:07:32Z","timestamp":1627344452000},"page":"1-10","source":"Crossref","is-referenced-by-count":9,"title":["Individual Load Forecasting for Multi-Customers with Distribution-aware Temporal Pooling"],"prefix":"10.1109","author":[{"given":"Eunju","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chan-Hyun","family":"Youn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2015.12.003"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2017.2688178"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2017.2718241"},{"key":"ref12","article-title":"Which tasks should be learned together in multi-task learning?","author":"standley","year":"2019"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2018.2807985"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2015.2493205"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2867681"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2017.2683461"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2017.07.006"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2018.01.015"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3152494.3152501"},{"key":"ref28","author":"bishop","year":"2006","journal-title":"Pattern Recognition and Machine Learning"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2017.2686012"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7966378"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2013.2277171"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2018.10.078"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRD.2013.2287032"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"157 633","DOI":"10.1109\/ACCESS.2019.2949065","article-title":"A Hybrid LSTM Neural Network for Energy Consumption Forecasting of Individual Households","volume":"7","author":"du","year":"2019","journal-title":"IEEE Access"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/W14-4012"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2910416"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2952917"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2017.2753802"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2018.2818167"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/273"},{"key":"ref22","first-page":"6467","article-title":"Gradient episodic memory for continual learning","author":"lopez-paz","year":"2017","journal-title":"Advances in Neural IInformation Processing Systems"},{"key":"ref21","article-title":"Auto-encoding variational bayes","author":"kingma","year":"2014","journal-title":"ICLRE"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2942353"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2869129"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2019.05.230"},{"key":"ref25","first-page":"1049","article-title":"Mark: Exploiting cloud services for cost-effective, slo-aware machine learning inference serving","author":"zhang","year":"2019","journal-title":"2019 USENIX Annual Technical Conference ( USENIX ATC 19)"}],"event":{"name":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications","location":"Vancouver, BC, Canada","start":{"date-parts":[[2021,5,10]]},"end":{"date-parts":[[2021,5,13]]}},"container-title":["IEEE INFOCOM 2021 - IEEE Conference on Computer Communications"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9488422\/9488423\/09488816.pdf?arnumber=9488816","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T15:43:37Z","timestamp":1652197417000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9488816\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,10]]},"references-count":30,"URL":"https:\/\/doi.org\/10.1109\/infocom42981.2021.9488816","relation":{},"subject":[],"published":{"date-parts":[[2021,5,10]]}}}